Co-scheduling Problem Research Articles

Ship traffic optimization method for solving the approach channel and lock co-scheduling problem of the Three Gorges Dam on the Yangzi River

Since the demands of waterway transport on the Yangzi River have been growing in recent years, traffic congestion at Three Gorges Dam (TGD) has become a very serious issue, which leads to long waiting time as well as environmental pollution. To improve the efficiency of the ship traffic scheduling, this paper investigates an approach channel and lock co-scheduling problem of the TGD on the Yangzi River. A new mathematical model is presented to optimize the lock chamber utilization rate, average waiting time and total energy consumption of ships, simultaneously. To solve this problem, a multi-objective metaheuristic algorithm (MOMA) based on fuzzy correlation entropy is proposed. In the algorithm, with a ship sequence encoding scheme, a pre-grouping panning approach is firstly designed for decoding. Then, a fitness evaluation mechanism using fuzzy correlation entropy is adopted to assess the solutions. Finally, an adaptive local reinforcement search strategy is introduced to improve local intensification ability. Various simulation experiments are carried out based on the real historical data at TGD. Computational results demonstrate the applicability and effectiveness of proposed MOMA. The result of this study is very useful for TGD to improve the ship traffic efficiency and save energy.

Discrete multi-objective artificial bee colony algorithm for green co-scheduling problem of ship lift and ship lock

This paper investigates a multi-objective green co-scheduling problem of ship lift and ship lock (GCP-SL&SL) at the Three Gorges Cascade Hub (TGCH). A mathematical model of GCP-SL&SL with objectives of the average utilizations rate of the lock chamber, average waiting time and total energy consumption of vessels, is proposed by separating it into three sub-problems: the facility assignment, lockage assignment and lockage operation scheduling. To solve this problem, a discrete multi-objective artificial bee colony (DMOABC) algorithm is developed. Within the DMOABC, a two-dimensional matrix encoding scheme is designed to encode and a group right-shift decoding scheme is specifically proposed to decode each food source. Then, a novel fitness evaluation mechanism based on fuzzy relative entropy is introduced to hand this multi-objective problem. Next, the food sources are improved from three aspects: (1) the employed bee phase uses new evolutionary operators for fast local search; (2) the onlooker bee phase adopts a modified tabu search for strong global search; (3) the scout bee phase embeds chemical reaction optimization for disturbing population. Finally, extensive experiments are conducted with the real data from historical traffic at TGCH. The results demonstrate our proposed algorithm is significantly better at solving the GCP-SL&SL than other five well-known multi-objective algorithms. The effect analysis under different scenarios indicates that the average waiting time of vessels at the dam is greatly reduced because of considering the synchronous moving process.

Schedulability Analysis for Coscheduling Real-Time Tasks on Multiprocessors

The real-time coscheduling problem, where tasks may have multiple phases executing on different types of processors, is known to be hard. The (already hard) self-suspending task scheduling simplifies the coscheduling problem by assuming that the latency a task may experience on the other type of processors is naturally bounded, which is unfortunately not true in practice. In this article, we present a novel analysis technique, namely, the vertical view analysis, for analyzing the schedulability of coscheduling sporadic tasks under global earliest-deadline-first (GEDF) on a heterogeneous multiprocessor consisting of two types of processors. We derive both hard (no deadline miss) and soft (bounded response times) real-time utilization-based tests. To the best of our knowledge, these results are the first-of-its-kind for the coscheduling problem and may allow real-time schedulability analysis to be carried out on more practical scenarios under heterogeneous computing.

Analytical and Numerical Evaluation of Co-Scheduling Strategies and Their Application

Applications in high-performance computing (HPC) may not use all available computational resources, leaving some of them underutilized. By co-scheduling, i.e., running more than one application on the same computational node, it is possible to improve resource utilization and overall throughput. Some applications may have conflicting requirements on resources and co-scheduling may cause performance degradation, so it is important to take it into account in scheduling decisions. In this paper, we formalize the co-scheduling problem and propose multiple scheduling strategies to solve it: an optimal strategy, an online strategy and heuristic strategies. These strategies vary in terms of the optimality of the solution they produce and a priori information about the system they require. We show theoretically that the online strategy provides schedules with a competitive ratio that has a constant upper limit. This allows us to solve the co-scheduling problem using heuristic strategies that approximate this online strategy. Numerical simulations show how heuristic strategies compare to the optimal strategy for different input systems. We propose a method for measuring input parameters of the model in practice and evaluate this method on HPC benchmark applications. We show the high accuracy of the measurement method, which allows us to apply the proposed scheduling strategies in the scheduler implementation.

A Coscheduling Model of Automated Single-Beam Cranes and AGVs in Assembly Workshop

The simultaneous scheduling of cranes and automated guided vehicles (AGVs) has been a critical problem, especially in traditional heavy-duty manufacturing enterprises. Previous studies mainly focus on the coscheduling problem for the container terminals rather than workshop. Hence, this study evaluated the coscheduling problem of automated single-beam cranes and AGVs incurred by an actual manufacturing environment. A joint scheduling model was established by considering the constraints of conflict-free, noncrossing, and safety distance as well as the feature of the material. This model considers the process and minimum makespan as the scheduling unit and optimization objective, respectively, including handling time and process time. Subsequently, a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) was developed, and a check-repair procedure was designed to guarantee the feasibility of optimal solution. Finally, numerical illustrative experiments and nonparametric tests were conducted to verify the effectiveness of the proposed model and algorithm, and the results show that the proposed algorithm has better performance than PSO and GA in solving the proposed model.

An enhanced Borg algorithmic framework for solving the hydro-thermal-wind Co-scheduling problem

An enhanced Borg (EBorg) algorithm has been proposed to solve the dual-objective short-term hydro-thermal-wind co-scheduling (HTW-CS) problem, aiming at minimizing the cost and emissions associated with electric power generation while satisfying various hydraulic and electric constraints. The sophisticatedly designed evolution framework of the EBorg consists of i) ε-dominance-based archive, ii) Pareto-dominance and crowding-distance-based population updating mechanism and iii) auto-adaptive multi-operator recombination, which guarantees the convergence capability and diversity and can avoid blindness selection of recombination operators. Meanwhile, a randomness-priority-based repairing constraint handling technique (CHT) is developed, and the performances of another two popular existing CHTs incorporated in the proposed search framework are compared and discussed. The proposed approaches are tested on the widely used HTW-CS case studies, and the results show that the proposed EBorg can achieve a decrease of cost and emissions compared with the existing methods. Additionally, energy performance in the case studies has shown an average 50% decrease of emissions with wind power integration, while the total cost is largely dependent on the cost coefficients related to wind uncertainty. Varying wind power cost coefficients and water inflow levels will result in different wind power and hydropower integration.

Coexistence Mechanism Between eMBB and uRLLC in 5G Wireless Networks

Ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for the maximum data rates. Owing to the trade-off among latency, reliability and spectral efficiency, sharing of radio resources between eMBB and uRLLC services, heads to a challenging scheduling dilemma. In this paper, we study the co-scheduling problem of eMBB and uRLLC traffic based upon the puncturing technique. Precisely, we formulate an optimization problem aiming to maximize the minimum expected achieved rate (MEAR) of eMBB user equipment (UE) while fulfilling the provisions of the uRLLC traffic. We decompose the original problem into two sub-problems, namely scheduling problem of eMBB UEs and uRLLC UEs while prevailing objective unchanged. Radio resources are scheduled among the eMBB UEs on a time slot basis, whereas it is handled for uRLLC UEs on a mini-slot basis. Moreover, for resolving the scheduling issue of eMBB UEs, we use penalty successive upper bound minimization (PSUM) based algorithm, whereas the optimal transportation model (TM) is adopted for solving the same problem of uRLLC UEs. Furthermore, a heuristic algorithm is also provided to solve the first sub-problem with lower complexity. Finally, the significance of the proposed approach over other baseline approaches is established through numerical analysis in terms of the MEAR and fairness scores of the eMBB UEs.

Improved co-scheduling of multi-layer printing path scanning for collaborative additive manufacturing

Additive manufacturing processes, especially those based on fused filament fabrication mechanism, have a low productivity. One solution to this problem is to adopt a collaborative additive manufacturing system that employs multiple printers/extruders working simultaneously to improve productivity by reducing the process makespan. However, very limited research is available to address the major challenges in the co-scheduling of printing path scanning for different extruders. Existing studies lack: (i) a consideration of the impact of sub-path partitions and simultaneous printing of multiple layers on the multi-extruder printing makespan; and (ii) efficient algorithms to deal with the multiple decision-making involved. This article develops an improved method by first breaking down printing paths on different printing layers into sub-paths and assigning these generated sub-paths to different extruders. A mathematical model is formulated for the co-scheduling problem, and a hybrid algorithm with sequential solution procedures integrating an evolutionary algorithm and a heuristic is customized to multiple decision-making in the co-scheduling for collaborative printing. The performance was compared with the most recent research, and the results demonstrated further makespan reduction when sub-path partition or the simultaneous printing of multiple layers is considered. This article discusses the impacts of process setups on makespan reduction, providing a quantitative tool for guiding process development.

A Co-Scheduling Problem of Ship Lift and Ship Lock at the Three Gorges Dam

Due to the unprecedentedly increasing demands of waterway transportation on the Yangtze River during the latest years, traffic congestion at the Three Gorges Dam (TGD) has becoming a serious problem. In busiest seasons, a vessel may need to wait up to 3 days for passing the TGD via the ship lift or the five-stage ship lock. In order to improve the navigation efficiency and the utilization of passing facilities, a co-scheduling problem of ship lift and ship lock at the TGD is modeled in this paper. Besides, the improvement on effectiveness, safety and fairness of the navigation scheduling by introducing the ship lift is taken into account. The mathematical model proposed is, by nature, a two-stage problem, where the first-stage variables determine the assignments of passing facilities and the second-stage variables schedule the vessels. In order to solve the proposed mathematical model, a hybrid metaheuristic algorithm is used at the first stage to determine the allocation of vessels to different passing facilities and to different lockage through the maximization of facility utilization rate. The second-stage problem is then solved by CPLEX. The proposed mathematical model and algorithm are validated through a set of numerical experiments and a case study. The results show both the navigation efficiency and the utilization of passing facilities can be improved by using the proposed methods.

Model and Algorithm for Co-scheduling of Stackers and Single RGV during retrieval Process in AS/RS

This paper studies the co-scheduling problem of the stackers and single rail guided vehicle system in automated storage and retrieval systems. We formulate the problem into a mixed integer linear programming model with the aim of minimizing the total retrieval time. In order to solve the problem, an improved genetic algorithm is proposed, and the properties of the optimal solution are applied to improve the offspring chromosomes. A lower bound of the total retrieval time is developed. The numerical experiment indicates that the proposed algorithm can solve the problem effectively. Compared with the solutions obtained by using first come first served, the total retrieval time obtained by the proposed algorithm is reduced by an average of 8.36%. The average deviation from the lower bound is 2.00%.

Exact and heuristic methods for optimizing lock-quay system in inland waterway

This paper focuses on lock and quay co-scheduling problem (LQCP) so that delay time of ships at a lock and time spent at the quay are minimized. The task can be regarded as a main problem where an alternative mode for a ship is determined by solving two sub-problems of lock scheduling and berth allocation. For the first time, the LQCP considers the discrete berth allocation of container ships and the mooring constraints of lock scheduling. A mixed integer linear programming (MILP) model is formulated for the LQCP and small-scale problems are solved by branch and bound method. In addition, fuzzy logic control based heuristic method is proposed to handle large-scale LQCP. Specifically, a fuzzy-controlled quantum inspired gravitational search algorithm is proposed to search optimal mode combinations for the main problem iteratively. In each iteration, Tabu search based multi-order best fit algorithm is proposed to solve lock scheduling sub-problem and an adaptive large neighborhood search algorithm is applied to solve berth allocation sub-problem. The MILP and heuristic methods are tested on 42 instances, in which the MILP is implemented in Gurobi 7.5.1. Experimental results indicate that the MILP model can handle different traffic situations. The proposed heuristic method shows tiny optimality gap for small-scale instances and outperforms Gurobi on most of medium-large scale instances with respect to solution quality and computation time. Furthermore, comparison between different heuristics on medium and large scale instances confirms that the fuzzy logic control based heuristic outperforms other heuristic methods.

A Binary Borg-Based Heuristic Method for Solving a Multi-Objective Lock and Transshipment Co-Scheduling Problem

The lock and transshipment co-scheduling problem (LTCP) is studied so that the delay time of ships at a dam as well as the extra cost of transshipment is minimized. A bi-objective optimization model of LTCP is proposed, which is decomposed to a main 0–1 optimization problem and two sub-problems, the lock scheduling and continuous berth allocation problem. Meanwhile, a binary Borg (B-Borg) multi-objective evolutionary algorithm is proposed to combine with an adaptive large neighborhood search and a multi-order best fit method to solve the LTCP. In order to evaluate the feasibility of the proposed LTCP model and the B-Borg-based heuristic, a large number of instances are generated by extracting the record from historical traffic at the Three Gorges Dam. The results show that the average delay of ships at the dam is reduced because of transshipment, among which the ships with lower extra transshipment costs tend to be more likely to be transshipped. Meanwhile, it indicates the number of ships that choose to be transshipped is mainly dependent on the level of traffic congestion as well as the available quay for transshipment. Furthermore, the performance of the B-Borg is confirmed superiority by comparing with non-dominated sorting genetic algorithm and decomposition-based multi-objective evolutionary algorithm in terms of solving LTCP.

An adaptive large neighbourhood search-based optimisation for economic co-scheduling of mobile robots

The growing energy consumption and environmental pressures call for economic and friendly green manufacturing. The paper creatively studies an economic part feeding scheduling problem with a cooperative mechanism to coordinate multiple mobile robots in the fullest sense. When solving the economic co-scheduling problem in mixed-model assembly lines, this paper jointly considers the objective of energy saving as well as the robot employment cost, which incorporates traditional performance criterion with growing energy concerns. In order to improve the performance and diversity of solutions, a multi-phase adaptive search (MPAS) algorithm is proposed which is integrated with clustering heuristics, specific destroy and repair rules, adaptive selection and perturbation strategy. Computational experiments are conducted in order to test and verify the effectiveness and efficiency of the proposed MPAS algorithm. Comparison tests are carried out between the proposed MPAS and two widely-applied benchmark algorithms. The results obtained in this study might be inspiring for future studies on energy-efficient cooperative scheduling topics. [Received 16 February 2018; Revised 22 May 2018; Accepted 22 June 2018]

Orthogonal Design-Based NSGA-III for the Optimal Lockage Co-Scheduling Problem

Lockage co-scheduling of Three Gorges Dam and Gezhou Dam (LCSTD) is a mixed-integer nonlinear optimization (MINO) problem. This paper establishes a multi-objective MINO model for the LCSTD problem, in which two criteria reflecting the interests of shippers on the one side and owners of the lock on the other side are considered as two parallel optimization objectives. The LCSTD problem is separated into three sub-problems. The first sub-problem concerns the lockage number and lockage direction determination, while the second and third sub-problems refer to lockage time optimization and ship placement, respectively. An orthogonal design-based non-dominated sorting genetic algorithm III (ONSGA-III) is presented, and then, we combine the ONSGA-III with a time-area series assignment method to optimize the LCSTD problem. Meanwhile, a heuristic adjustment strategy is proposed according to the structure of the lockage time optimization sub-problem to enhance the exploration ability of ONSGA-III. Furthermore, a compromise solution is chosen from the achieved non-dominated Pareto solutions by utilizing a membership function. Finally, the method is tested on the instances, which are extracted from historical data at Three Gorges Dam and Gezhouba Dam. The Pareto solutions achieved by the proposed method are compared with those of other methods. The simulation results demonstrate that the proposed method is efficient for solving the LCSTD problem.

Deadline and Period Assignment for Update Transactions in Co-Scheduling Environment

Deriving deadline and period for update transactions to maintain temporal consistency has long been recognized as an important problem in real-time database research. Despite years of active study, most of the past work only focuses on the scheduling of update transactions, and neglects the impact of control transactions by assuming an Update First policy where control transactions are always assigned lower priorities than the update transactions. On the other hand, most existing work on co-scheduling of update and control transactions has been focused on meeting the deadlines of all the control transactions while maximizing the quality of data of the real-time data objects. In this paper, we study the co-scheduling problem of update and control transactions by satisfying the deadline constraints of control transactions and the temporal validity constraints of update transactions simultaneously. Specifically, we consider the problem of how to derive deadline and period for update transactions to maintain the temporal consistency of real-time data objects, while guaranteeing the hybrid transaction set to be EDF-schedulable. To address this problem, we first borrow the idea from $\mathsf{minD}$ [14] to derive a solution called $\mathsf{minD}^\ast$ , which can compute deadline and period for update transactions effectively. Next, based on a sufficient condition to derive the minimum possible deadline for each update transaction, we propose a more efficient algorithm Minimum Deadline Calculation ( $\mathsf{MDC}$ ), which can guarantee to derive a solution, given that one does exist. Finally, the effectiveness and efficiency of the proposed algorithms are validated through extensive simulation experiments.

Optimization of Duplication-Based Schedules on Network-on-Chip Based Multi-Processor System-on-Chips

Many applications such as streaming applications are both computation and communication intensive. The Multi-Processor System-on-Chip (MPSoC) based on Network-on-Chip (NoC) outperforms the multiprocessors with bus-based networking architecture in communication bandwidth and scalability, making it a better choice for implementing systems running these applications. It's important to schedule both the computation and communication onto processors and the networking architecture so as to satisfy the stringent timing requirements. To reduce or avoid inter-processor communication, task duplication has been employed in scheduling. Most of the available techniques for the duplication-based scheduling problem use heuristics to solve the problem, and seldom has any work studied further improving the schedule performance, despite the fact that the heuristic cannot provide quality guarantee. To fill in this gap, this paper introduces a duplication and mapping constrained task-communication co-scheduling problem that assumes the duplication strategy and task-to-processor mapping are known a priory, and proposes two Integer Linear Programming (ILP) formulations, i.e., CF-ILP and CA-ILP, to solve two editions of this problem, i.e., the contention-free problem and the contention-aware problem. The proposed ILP formulations optimize the ordering and timing of the communication and computation, thus improving the performance. Both synthesized and real applications are tested on a set of platforms to evaluate the performance of the proposed methods. The experimental results demonstrate the effectiveness of the proposed methods.

An optimal energy co-scheduling framework for smart buildings

The Heating, Ventilation and Air Conditioning (HVAC) system accounts for nearly half of the energy consumption of a typical building. Additionally, the need for HVAC changes over hours and days as does the electric energy price. Level of comfort of the building occupants is, however, a primary concern, which tends to overwrite pricing. Dynamic HVAC control under a dynamic energy pricing model while meeting an acceptable level of occupants' comfort is thus critical to achieve energy efficiency in buildings in a sustainable manner. Finally, there is the possibility that the building is equipped with some renewable sources of power such as solar panels mounted on the rooftop. The presence of Hybrid Electrical Energy Storage (HEES) system in a target building would enable peak power shaving by adopting a suitable charging and discharging schedule for each Electrical Energy Storage (EES) element, while simultaneously meeting building energy efficiency and user comfort requirements. Achieving this goal requires detailed information (or predictions) about the amount of local power generation from the renewable source plus the power consumption load of the building.This paper addresses the co-scheduling problem of HVAC control and HEES system management to achieve energy-efficient smart buildings, while also accounting for the degradation of the battery state-of-health during charging and discharging operations (which in turn determines the amortized cost of owning and utilizing a battery storage system). A time-of-use dynamic pricing scenario is assumed and various energy loss components are considered, including power dissipation in the power conversion circuitry, the rate capacity effect in the batteries, and the self-discharge in the super-capacitor. A global optimization framework targeting the entire billing cycle is presented and an adaptive co-scheduling algorithm is provided to dynamically update the optimal HVAC air flow control and the HEES system management in each time slot during the billing cycle to mitigate the prediction error of unknown parameters. Experimental results show that the proposed algorithm achieves up to 10% in the total electric utility cost compared with some baseline methods.

Adaptive co-scheduling for periodic application and update transactions in real-time database systems

In this paper, we study the co-scheduling problem of periodic application transactions and update transactions in real-time database systems for surveillance of critical events. To perform the surveillance functions effectively, it is important to meet the deadlines of the application transactions and maintain the quality of the real-time data objects for their executions. Unfortunately, these two goals are conflicting and difficult to be achieved at the same time. To address the co-scheduling problem, we propose a real-time co-scheduling algorithm, called Adaptive Earliest Deadline First Co-Scheduling (AEDF-Co). In AEDF-Co, a dynamic scheduling approach is adopted to adaptively schedule the update and application jobs based on their deadlines. The performance goal of AEDF-Co is to determine a schedule for given sets of periodic application and update transactions such that the deadline constraints of all the application transactions are satisfied and at the same time the quality of data (QoD) of the real-time data objects is maximized. Extensive simulation experiments have been performed to evaluate the performance of AEDF-Co. The results show that by adaptively adjusting the release times of update jobs and scheduling the update and application jobs dynamically based on their urgencies, AEDF-Co is effective in achieving the performance goals and maximizing the overall system performance.